ROBUST REPEATABLE MONITORING WITH EIT

Lead Research Organisation: University of Edinburgh
Department Name: Sch of Engineering

Abstract

Mechanical ventilation in the intensive care unit (ICU) is often a life-saving intervention for patients suffering from acute respiratory distress syndrome (ARDS) and acute lung injury (ALI). However, in recent years, it has become clear that mechanical ventilation can exacerbate lung damage and may even be the primary factor in lung injury e.g. ventilator-induced lung injury (VILI) and ventilator associated pneumonia (VAP). Consequently, lung protective ventilation (LPV) strategies have been developed, including positive end expiratory pressure (PEEP) and high frequency oscillatory ventilation (HFOV). Despite the use of LPV, the mortality associated with ARDS and ALI is still great, with reported rates between 33 and 55%.

Thoracic electrical impedance tomography (EIT) has emerged as a promising new imaging tool for bedside use. It is a non-invasive technique which provides the internal conductivity distribution based on electrical measurements on the skin. Existing research and commercial EIT systems have typically arranged measurement electrodes in rings around the thorax and produced two-dimensional image slices based on over-simplified two-dimensional generic shapes e.g. circular or elliptical cross-sections. Unfortunately, such an approach creates significant image artefacts and poor levels of repeatability. In order to eliminate this problem, EIT measurements and image reconstruction must be treated as a fully three-dimensional problem, taking into account the electrode positions and the body shape of the patient. One of the aims of this project is to provide reliable and robust three-dimensional EIT image reconstruction based on 3D informed body shape acquired from other modalities such as magnetic resonance imaging (MRI) and x-ray computed tomography.

Currently, there exists no consensus amongst clinicians on how to optimize PEEP ventilator settings. Improved knowledge about the distribution of ventilation will enable clinicians to set more appropriate ventilation parameters in order to reduce the potential occurrence of VILI. In particular, the value of bedside EIT monitoring for determining the effect of PEEP in comparison with traditional indirect methods, such as arterial oxygenation or global tidal volumes, is still largely unknown. Additionally, despite the clear link between lung mechanics and regional ventilation, ventilator settings are normally based on blood gases and pressure-volume curves rather than measurements of lung mechanics. A significant aim of this project is to provide clinicians with robust image-based lung mechanics models which will further aid the validation of EIT in the ICU.
Previous clinical EIT studies have generally utilized EIT instruments operating at relatively slow frame rates, inflexible drive and measurement strategies along with limited number of electrodes. In this project, we will develop a state-of-the-art fast 3D EIT system using advanced measurement techniques which have not yet had a significant impact in the clinical environment. In fact, there has never been a substantial investigation of EIT in the ICU which has drawn together the unique combination of high quality EIT measurements, three-dimensional image reconstruction and the validation of lung mechanics modelling.

We propose to establish a world-leading capability in the measurement and imaging by EIT in the ICU. This project builds upon the substantial expertise in EIT at the University of Manchester (UoM) and the UK's leading clinical group in applied thoracic EIT at Kings College London and Guy's and St Thomas' NHS Trust. We will also build upon the world-leading capability at UoM in modelling and dynamic MRI acquisition including oxygen-enhanced and dynamic contrast-enhanced MRI. The proposed project registers strongly on the EPSRC research theme of healthcare technologies.

Publications

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Liu S (2020) Time Sequence Learning for Electrical Impedance Tomography Using Bayesian Spatiotemporal Priors in IEEE Transactions on Instrumentation and Measurement

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Ouypornkochagorn T (2019) A Comparison of Bound-Constrained and Positivity-Constrained Optimization Method to Estimate Head Tissue Conductivities by Scalp Voltage Information in International journal of electrical and computer engineering systems

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Ouypornkochagorn T (2015) Tackling modelling error in the application of electrical impedance tomography to the head. in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

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Ouypornkochagorn T (2019) In Vivo Estimation of Head Tissue Conductivities Using Bound Constrained Optimization. in Annals of biomedical engineering

 
Description First demonstration of imaging of cerebral haemodynamics by Electrical Impedance Tomography.
Demonstration of World-leading performance of EIT for lung imaging.
Exploitation Route Multiple.
Sectors Healthcare

 
Description To design further EIT systems (e.g. multi-frequency) with further sensitivity and utility.
First Year Of Impact 2017
Sector Healthcare
Impact Types Economic